Orchestrating data for machine learning pipelines

Machine learning (ML) workloads require efficient infrastructure to yield rapid results. Model training relies heavily on large data sets. Funneling this data from storage to the training cluster is the first step of any ML workflow, which significantly impacts the efficiency of model training.

Data and AI platform engineers have long been concerned with managing data with these questions in mind:

• Data accessibility: How to make training data accessible when data spans multiple sources and data is stored remotely?


• Data …

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